 A new risk evaluation method for supply chain based on convolution neural networkHuanle Han and Lianguang Mo International Journal of Applied Systemic Studies, 2024, vol. 11, issue 2, 121-137 Abstract: In the traditional supply chain risk assessment methods, the significance of selecting evaluation indexes is low, which leads to the problems of low fitting and poor accuracy of risk assessment results. This paper proposes a new convolution neural network method to measuring the risk of supply chain. All risk factors in the supply chain are analysed to clarify the relationship between different factors. The determination of the overall risk assessment index is done by identifying the coordination risk, logistics risk, information risk, and capital risk. The determination of the individual risk index is done by identifying the cost-profit rate, product qualification rate, and order lead time of the supplier risk. The manufacturing cost, product development cycle, and product flexibility are determined in the manufacturer's risk. On this basis, the measurement index system of this link is designed, though the convolution neural network to measure this measurement index system. Through analysis the highest fitting degree of this method was about 94%, the highest true rate is about 0.91, the lowest false positive rate is about 0.09. Keywords: convolution neural network; influencing factors; supply chain; risk evaluation; evaluation index. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijassi:v:11:y:2024:i:2:p:121-137 Access Statistics for this article More articles in International Journal of Applied Systemic Studies from Inderscience Enterprises Ltd |
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